• Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models View PDF HTML (experimental)Abstract:Generating realistic synthetic populations is essential for agent-based models (ABM) in transportation and urban planning. • Current methods face two major limitations. • First, many rely on a single dataset or follow a sequential data fusion and generation process, which means they fail to capture the complex interplay between features. • Second, these approaches struggle with sampling zeros (valid but unobserved attribute combinations) and structural zeros (infeasible combinations due to logical constraints), which reduce the diversity and feasibility of the generated data. • This study proposes a novel method to simultaneously integrate and synthesize multi-source datasets using a Wasserstein Generative Adversarial Network (WGAN) with gradient penalty. • This joint learning method improves both the diversity and feasibility of synthetic data by defining a regularization term (inverse gradient penalty) for the generator loss function.
Article Summaries:
- Computer Science > Artificial Intelligence [Submitted on 17 Feb 2026] Title:Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models View PDF HTML (experimental)Abstract:Generating realistic synthetic populations is essential for agent-based models (ABM) in transportation and urban planning. Current methods face two major limitations. First, many rely on a single dataset or follow a sequential data fusion and generation process, which means they fail to capture the complex interplay between features. Second, these approaches struggle with s
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